The goal is to present nuclear weapon test data on a map. But first, an extensive data cleaning was needed, see document NuclearWeaponTests_Cleaning.Rmd. Together with the map, different plots are presented to present differences and similarities between tests by different testing parties.
Import libraries
#install.packages("plotly") # needed only if plotly has not been installed already
library(plotly)
library(ggplot2)
library(dplyr)Set some constants for the number of rows shown in tables.
ROWS = 30 # how many data rows are displayed Reading in data. Data is downloaded from http://nuclearweaponarchive.org/Library/Catalog, and it has been saved as nuclearweapondata.txt. Updated version can be found from https://www.batguano.com/nuclear/nuccatalog.html.
df_data <- read.csv("nuclearweapondata_cleaned.csv")The following data is presented in the file:
A glipse of the data to confirm everything looks normal:
head(df_data, 10) ## X Date TestParty TestParty_Descr Site
## 1 1 1945-07-16 US United States ANM
## 2 2 1945-08-05 US United States HRJ
## 3 3 1945-08-09 US United States NGJ
## 4 4 1946-06-30 US United States BKN
## 5 5 1946-07-24 US United States BKN
## 6 6 1948-04-14 US United States ENW
## 7 7 1948-04-30 US United States ENW
## 8 8 1948-05-14 US United States ENW
## 9 9 1949-08-29 CP Soviet Union <NA>
## 10 10 1951-01-27 US United States NTSF
## Site_Descr TestType TestType_Descr SeismBody
## 1 Alamogordo, New Mexico TOWR Tower NA
## 2 Hiroshima, Japan AIRD Airdrop NA
## 3 Nagasaki, Japan AIRD Airdrop NA
## 4 Bikini Atoll, Marshall Islands AIRD Airdrop NA
## 5 Bikini Atoll, Marshall Islands UNDW Underwater NA
## 6 Enewetak Atoll, Marshall Islands TOWR Tower NA
## 7 Enewetak Atoll, Marshall Islands TOWR Tower NA
## 8 Enewetak Atoll, Marshall Islands TOWR Tower NA
## 9 <NA> TOWR Tower NA
## 10 Nevada Test Site, Frenchman Flat AIRD Airdrop NA
## Yield Lat Lng Purpose Purpose_Descr Device Device_Descr
## 1 21 33.67500 -106.4750 WR Weapons related P Primarly Pu239
## 2 15 NA NA ** War U Primarly U235
## 3 21 NA NA ** War P Primarly Pu239
## 4 21 11.00000 165.0000 WE Weapons effects P Primarly Pu239
## 5 21 11.04471 165.1054 WE Weapons effects P Primarly Pu239
## 6 37 11.00000 162.0000 WR Weapons related <NA> <NA>
## 7 49 11.00000 162.0000 WR Weapons related <NA> <NA>
## 8 18 11.00000 162.0000 WR Weapons related U Primarly U235
## 9 NA 48.00000 76.0000 <NA> <NA> P Primarly Pu239
## 10 1 37.00000 -116.0000 WR Weapons related <NA> <NA>
## Name
## 1 TRINITY
## 2 LITTLEBO
## 3 FAT MAN
## 4 ABLE
## 5 BAKER
## 6 XRAY
## 7 YOKE
## 8 ZEBRA
## 9 JOE 1
## 10 ABLEChange date to POSIXct format and add a year column
df_data$Date <- as.POSIXct(df_data$Date)
df_data$Year <- format(df_data$Date,"%Y")
head(df_data$Year, 10) ## [1] "1945" "1945" "1945" "1946" "1946" "1948" "1948" "1948" "1949" "1951"Create text to show when hovering on map.
df_data$hover <- with(df_data, paste("Test Site:", Site_Descr, "<br>", "Date:", Date, '<br>',
"Test party:", TestParty_Descr, "<br>", "Name:", Name, '<br>',
"Device:", Device_Descr, "<br>", "Yield:", Yield, "<br>",
"Purpose:", Purpose_Descr, "<br>", "Type:", TestType_Descr
))Print map:
g <- list( # define map elements
showland = TRUE,
landcolor = "White",
showocean = TRUE,
oceancolor = "LightBlue",
showlakes = TRUE,
lakecolor = "LightBlue",
showrivers = TRUE,
rivercolor = "LightBlue",
showcountries = TRUE,
countrycolor = "DarkGray",
resolution = 50,
projection = list( type = 'natural earth')
)
# define data shown on map
fig <- plot_geo(df_data, lat = ~Lat, lon = ~Lng)
fig <- fig %>% add_markers(text = df_data$hover,
color = df_data$TestParty_Descr,
size = I(25))
# define layout
fig <- fig %>% layout(
title = 'Nuclear tests',
geo = g
)
fig## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: Ignoring 117 observationsNote that most locations on the map are approximate.
Get frequencies for the plot; Get number of tests by testing party
totCounts <- data.frame(df_data %>% group_by(TestParty_Descr) %>% summarise(NoEvents = n()))
totCounts <- na.omit(totCounts) # remove NaNs
totCounts## TestParty_Descr NoEvents
## 1 China 46
## 2 France 200
## 3 India 1
## 4 Israel (Putative) 1
## 5 Soviet Union 587
## 6 United Kingdom 44
## 7 United States 1147Create plot
fig <- plot_ly(totCounts, labels = ~TestParty_Descr, values = ~NoEvents, type = 'pie',
textinfo = "percent", hoverinfo = 'text', textposition = 'inside',
text = ~paste(TestParty_Descr, ': ', NoEvents))
fig <- fig %>% layout(title = 'Nuclear Tests by Testing Party')
figGet frequencies for the plot; Get tests by year and testing party.
eventCounts <- data.frame(df_data %>% group_by(Year,TestParty_Descr) %>% summarise(NoEvents = n()))## `summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.head(eventCounts, 10)## Year TestParty_Descr NoEvents
## 1 1945 United States 3
## 2 1946 United States 2
## 3 1948 United States 3
## 4 1949 Soviet Union 1
## 5 1951 Soviet Union 2
## 6 1951 United States 16
## 7 1952 United Kingdom 1
## 8 1952 United States 10
## 9 1953 Soviet Union 2
## 10 1953 United Kingdom 2Create plot
p <- ggplot(eventCounts, aes(fill=TestParty_Descr, y=NoEvents, x=Year)) +
geom_bar(position="stack", stat="identity", width = 0.9) +
theme(legend.title = element_blank())
fig <- ggplotly(p)
fig <- fig %>% layout(title = 'Number of Nuclear Tests Yearly',
xaxis = list(tickangle = 90, title = ""),
yaxis = list(title = ""))
figGet tests by device type and testing party
df_testTypes <- data.frame(df_data %>% group_by(TestType_Descr,TestParty_Descr) %>% summarise(noTestTypes = n()))## `summarise()` has grouped output by 'TestType_Descr'. You can override using the `.groups` argument.head(df_testTypes, 10)## TestType_Descr TestParty_Descr noTestTypes
## 1 Airdrop China 7
## 2 Airdrop France 3
## 3 Airdrop Soviet Union 2
## 4 Airdrop United Kingdom 8
## 5 Airdrop United States 54
## 6 Artillery Shell United States 1
## 7 Atmosphere China 13
## 8 Atmosphere France 20
## 9 Atmosphere Soviet Union 112
## 10 Balloon France 20Create plot
p <- ggplot(df_testTypes, aes(fill=TestParty_Descr, y=noTestTypes, x=TestType_Descr)) +
geom_bar(position="stack", stat="identity", width = 0.9) +
theme(legend.title = element_blank())
fig <- ggplotly(p)
fig <- fig %>% layout(title = 'Number of Tests by Type and Testing Party',
xaxis = list(tickangle = 45,title = ""),
yaxis = list(title = ""))
figGet tests by device type and testing party
df_purposes <- data.frame(df_data %>% group_by(Purpose_Descr,TestParty_Descr) %>% summarise(noPurposes = n()))## `summarise()` has grouped output by 'Purpose_Descr'. You can override using the `.groups` argument.head(df_purposes, 10)## Purpose_Descr TestParty_Descr noPurposes
## 1 Peaceful (engineering) Soviet Union 12
## 2 Peaceful (engineering) United States 36
## 3 Safety United States 26
## 4 Seismic detection United States 7
## 5 War United States 2
## 6 Weapons effects France 2
## 7 Weapons effects United States 101
## 8 Weapons related France 159
## 9 Weapons related United Kingdom 2
## 10 Weapons related United States 956Create plot
p <- ggplot(df_purposes, aes(fill=TestParty_Descr, y=noPurposes, x=Purpose_Descr)) +
geom_bar(position="stack", stat="identity", width = 0.9) +
theme(legend.title = element_blank())
fig <- ggplotly(p)
fig <- fig %>% layout(title = 'Number of Tests by Purpose and Testing Party',
xaxis = list(tickangle = 45, title = ""),
yaxis = list(title = ""))
fig